Characterization of DESI fiber assignment incompleteness effect on 2-point clustering and mitigation methods for DR1 analysis

TL;DR

This paper analyzes the impact of fiber assignment incompleteness in DESI DR1 on galaxy clustering measurements, introduces a fast emulator for simulation, and evaluates mitigation techniques to recover true clustering signals.

Contribution

It introduces the FFA emulator for simulating fiber assignment effects and demonstrates effective mitigation methods, including PIP weights, for accurate clustering analysis.

Findings

PIP weights with angular upweighting recover true clustering.

FFA emulator accurately simulates fiber assignment effects.

First power spectrum measurement with PIP weights from real data.

Abstract

We present an in-depth analysis of the fiber assignment incompleteness in the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1). This incompleteness is caused by the restricted mobility of the robotic fiber positioner in the DESI focal plane, which limits the number of galaxies that can be observed at the same time, especially at small angular separations. As a result, the observed clustering amplitude is suppressed in a scale-dependent manner, which, if not addressed, can severely impact the inference of cosmological parameters. We discuss the methods adopted for simulating fiber assignment on mocks and data. In particular, we introduce the fast fiber assignment (FFA) emulator, which was employed to obtain the power spectrum covariance adopted for the DR1 full-shape analysis. We present the mitigation techniques, organised in two classes: measurement stage and model stage. We then use high fidelity mocks as a reference to quantify both the accuracy of the FFA emulator and the effectiveness of the different measurement-stage mitigation techniques. This complements the studies conducted in a parallel paper for the model-stage techniques, namely the $\theta$-cut approach. We find that pairwise inverse probability (PIP) weights with angular upweighting recover the "true" clustering in all the cases considered, in both Fourier and configuration space. Notably, we present the first ever power spectrum measurement with PIP weights from real data.